Camera agnostic depth network

ABSTRACT

A method for monocular depth/pose estimation in a camera agnostic network is described. The method includes projecting lifted 3D points onto an image plane according to a predicted ray vector based on a monocular depth model, a monocular pose model, and a camera center of a camera agnostic network. The method also includes predicting a warped target image from a predicted depth map of the monocular depth model, a ray surface of the predicted ray vector, and a projection of the lifted 3D points according to the camera agnostic network.

CROSS-REFERENCE TO RELATED APPLICATION

This application is a continuation of U.S. Provisional patentapplication Ser. No. 16/904,444, filed Jun. 17, 2020, and titled “CAMERAAGNOSTIC DEPTH NETWORK,” the disclosure of which is expresslyincorporated by reference herein in its entirety.

BACKGROUND Field

Certain aspects of the present disclosure generally relate to machinelearning and, more particularly, a camera agnostic depth network.

Background

Autonomous agents (e.g., vehicles, robots, etc.) rely on machine visionfor sensing a surrounding environment by analyzing areas of interest inimages of the surrounding environment. Although scientists have spentdecades studying the human visual system, a solution for realizingequivalent machine vision remains elusive. Realizing equivalent machinevision is a goal for enabling truly autonomous agents. Machine vision isdistinct from the field of digital image processing because of thedesire to recover a three-dimensional (3D) structure of the world fromimages and using the 3D structure for fully understanding a scene. Thatis, machine vision strives to provide a high-level understanding of asurrounding environment, as performed by the human visual system.

In operation, autonomous agents may rely on a trained convolutionalneural network (CNN) to identify objects within areas of interest in a2D image of a surrounding scene of the autonomous agent. For example, aCNN may be trained to identify and track objects captured by one or moresensors, such as light detection and ranging (LIDAR) sensors, sonarsensors, red-green-blue (RGB) cameras, RGB-depth (RGB-D) cameras, andthe like. The sensors may be coupled to, or in communication with, adevice, such as an autonomous vehicle. Object detection applications forautonomous vehicles, however, are limited to analyzing these 2D sensorimages, which omit depth information of our 3D real world.

Depth estimation from 2D images captured by a single camera (e.g.,monocular) of an autonomous agent may be referred to as monocular depthestimation. Current monocular depth estimation methods, however, rely onthe joint learning of depth and pose networks, using a proxy photometricloss that enables the use of geometric cues as the single source ofsupervision. Unfortunately, these depth and pose estimations are usuallyonly useful for the type of camera that provided the source data forthese estimations.

SUMMARY

A method for monocular depth/pose estimation in a camera agnosticnetwork is described. The method includes projecting lifted 3D pointsonto an image plane according to a predicted ray vector based on amonocular depth model, a monocular pose model, and a camera center of acamera agnostic network. The method also includes predicting a warpedtarget image from a predicted depth map of the monocular depth model, aray surface of the predicted ray vector, and a projection of the lifted3D points according to the camera agnostic network.

A non-transitory computer-readable medium having program code recordedthereon for monocular depth/pose estimation in a camera agnostic networkis described. The program code is executed by a processor. Thenon-transitory computer-readable medium includes program code to projectlifted 3D points onto an image plane according to a predicted ray vectorbased on a monocular depth model, a monocular pose model, and a cameracenter according to the camera agnostic network. The non-transitorycomputer-readable medium also includes program code to predict a warpedtarget image from a predicted depth map of the monocular depth model, aray surface of the predicted ray vector, and a projection of the lifted3D points according to the camera agnostic network.

A system for monocular depth/pose estimation in a camera agnosticnetwork is described. The system includes a pose network to lift 3Dpoints from image pixels of a target image according to one or morecontext images and to project the lifted 3D points onto an image planeaccording to a predicted ray vector based on a monocular depth model, amonocular pose model, and a camera center according to the cameraagnostic network. The system further includes a view synthesis block topredict a warped target image from a predicted depth map of themonocular depth model, a ray surface of the predicted ray vector, and aprojection of the lifted 3D points according to the camera agnosticnetwork.

This has outlined, rather broadly, the features and technical advantagesof the present disclosure in order that the detailed description thatfollows may be better understood. Additional features and advantages ofthe present disclosure will be described below. It should be appreciatedby those skilled in the art that the present disclosure may be readilyutilized as a basis for modifying or designing other structures forcarrying out the same purposes of the present disclosure. It should alsobe realized by those skilled in the art that such equivalentconstructions do not depart from the teachings of the present disclosureas set forth in the appended claims. The novel features, which arebelieved to be characteristic of the present disclosure, both as to itsorganization and method of operation, together with further objects andadvantages, will be better understood from the following descriptionwhen considered in connection with the accompanying figures. It is to beexpressly understood, however, that each of the figures is provided forthe purpose of illustration and description only and is not intended asa definition of the limits of the present disclosure.

BRIEF DESCRIPTION OF THE DRAWINGS

The features, nature, and advantages of the present disclosure willbecome more apparent from the detailed description set forth below whentaken in conjunction with the drawings in which like referencecharacters identify correspondingly throughout.

FIG. 1 illustrates an example implementation of designing a system usinga system-on-a-chip (SOC) for a camera agnostic depth network, inaccordance with aspects of the present disclosure.

FIG. 2 is a block diagram illustrating a software architecture that maymodularize functions for a camera agnostic depth network, according toaspects of the present disclosure.

FIG. 3 is a diagram illustrating an example of a hardware implementationfor a camera agnostic monocular depth system, according to aspects ofthe present disclosure.

FIGS. 4A-4C show drawings illustrating self-supervised monocular depthand pose estimation for an array of cameras, according to aspects of thepresent disclosure.

FIG. 5 is a block diagram illustrating a self-supervised monoculardepth/pose estimation framework based on an agnostic camera network,according to aspects of the present disclosure.

FIGS. 6A and 6B illustrate lifting and projection operations for astandard pinhole model in FIG. 6A and a camera agnostic model in FIG.6B, according to aspects of the present disclosure.

FIG. 7 is a flowchart illustrating a method for monocular visualodometry based on learned, depth-aware keypoints, according to aspectsof the present disclosure.

DETAILED DESCRIPTION

The detailed description set forth below, in connection with theappended drawings, is intended as a description of variousconfigurations and is not intended to represent the only configurationsin which the concepts described herein may be practiced. The detaileddescription includes specific details for the purpose of providing athorough understanding of the various concepts. It will be apparent tothose skilled in the art, however, that these concepts may be practicedwithout these specific details. In some instances, well-known structuresand components are shown in block diagram form in order to avoidobscuring such concepts.

Based on the teachings, one skilled in the art should appreciate thatthe scope of the present disclosure is intended to cover any aspect ofthe present disclosure, whether implemented independently of or combinedwith any other aspect of the present disclosure. For example, anapparatus may be implemented or a method may be practiced using anynumber of the aspects set forth. In addition, the scope of the presentdisclosure is intended to cover such an apparatus or method practicedusing other structure, functionality, or structure and functionality, inaddition to or other than the various aspects of the present disclosureset forth. It should be understood that any aspect of the presentdisclosure disclosed may be embodied by one or more elements of a claim.

Although particular aspects are described herein, many variations andpermutations of these aspects fall within the scope of the presentdisclosure. Although some benefits and advantages of the preferredaspects are mentioned, the scope of the present disclosure is notintended to be limited to particular benefits, uses, or objectives.Rather, aspects of the present disclosure are intended to be broadlyapplicable to different technologies, system configurations, networks,and protocols, some of which are illustrated by way of example in thefigures and in the following description of the preferred aspects. Thedetailed description and drawings are merely illustrative of the presentdisclosure, rather than limiting the scope of the present disclosurebeing defined by the appended claims and equivalents thereof.

Monocular depth and pose estimation are important perception tasks inthe area of autonomous agents, such as driverless cars and robots, whichare quickly evolving and have become a reality in this decade. Depthestimation from 2D images captured by a single camera (e.g., monocular)of an autonomous agent may be referred to as monocular depth estimation.Current monocular depth estimation methods, however, rely on the jointlearning of depth and pose networks, using a proxy photometric loss thatenables the use of geometric cues as the single source of supervision.Unfortunately, these depth and pose estimations are usually only usefulfor the type of camera that provided the source data for theseestimations.

In particular, existing monocular depth estimation methods rely on jointlearning of depth and pose networks. The joint learning of depth andpose networks relies on a proxy photometric loss that enables the use ofgeometric cues as a single source of supervision. Nevertheless, thephotometric loss uses camera information (e.g., intrinsic informationand/or extrinsic information) to synthesize a source image as a warpedversion of the target image. Unfortunately, depth and pose networks mayonly use the images themselves as input rather than photometric lossbased on intrinsic/extrinsic camera information.

Aspects of the present disclosure are directed to developing standarddepth and pose estimations that can be utilized across a vast array ofdifferent cameras, which may be referred to as camera agnostic depthnetworks. The present disclosure provides an improvement over currenttechnology by allowing training of monocular depth and pose models inmuch larger datasets. The larger datasets may leverage information fromany camera in a similar way by projecting this information into a cameraagnostic configuration. One aspect of the present disclosure modifiesstandard depth and pose estimations to incorporate camera information.

Aspects of the present disclosure provide a modification to standarddepth and pose estimations by incorporating camera information.Incorporating camera information into standard depth and poseestimations leads to estimates that are much more robust. The modifieddepth and pose estimations use a network of different cameras, both attraining and testing time. This camera network enables training ofmonocular depth and pose models using much larger datasets. According toaspects of the present disclosure, the larger datasets leverageinformation from any camera in a similar way by projecting thisinformation into a camera agnostic configuration. The camera informationincludes intrinsic and extrinsic camera information. For example,extrinsic parameters may define the location and orientation of thecamera with respect to a world frame. Intrinsic parameters may allow amapping between camera coordinates and pixel coordinates in an imageframe.

FIG. 1 illustrates an example implementation of the aforementionedsystem and method for a camera agnostic depth network, using asystem-on-a-chip (SOC) 100 of an ego vehicle 150. The SOC 100 mayinclude a single processor or multi-core processors (e.g., a centralprocessing unit (CPU) 102), in accordance with certain aspects of thepresent disclosure. Variables (e.g., neural signals and synapticweights), system parameters associated with a computational device(e.g., neural network with weights), delays, frequency bin information,and task information may be stored in a memory block. The memory blockmay be associated with a neural processing unit (NPU) 108, a CPU 102, agraphics processing unit (GPU) 104, a digital signal processor (DSP)106, a dedicated memory block 118, or may be distributed across multipleblocks. Instructions executed at a processor (e.g., CPU 102) may beloaded from a program memory associated with the CPU 102 or may beloaded from the dedicated memory block 118.

The SOC 100 may also include additional processing blocks configured toperform specific functions, such as the GPU 104, the DSP 106, and aconnectivity block 110, which may include fourth generation long termevolution (4G LTE) connectivity, unlicensed Wi-Fi connectivity, USBconnectivity, Bluetooth® connectivity, and the like. In addition, amultimedia processor 112 in combination with a display 130 may, forexample, classify and categorize poses of objects in an area ofinterest, according to the display 130 illustrating a view of a vehicle.In some aspects, the NPU 108 may be implemented in the CPU 102, DSP 106,and/or GPU 104. The SOC 100 may further include a sensor processor 114,image signal processors (ISPs) 116, and/or navigation 120, which may,for instance, include a global positioning system.

The SOC 100 may be based on an Advanced Risk Machine (ARM) instructionset or the like. In another aspect of the present disclosure, the SOC100 may be a server computer in communication with the ego vehicle 150.In this arrangement, the ego vehicle 150 may include a processor andother features of the SOC 100. In this aspect of the present disclosure,instructions loaded into a processor (e.g., CPU 102) or the NPU 108 ofthe ego vehicle 150 may include code for a camera agnostic depthnetwork, in an image captured by the sensor processor 114. Theinstructions loaded into a processor (e.g., CPU 102) may also includecode for planning and control (e.g., intention prediction of the egovehicle) in response to detecting ego-motion of the ego vehicle based onan image captured by the sensor processor 114.

FIG. 2 is a block diagram illustrating a software architecture 200 thatmay modularize functions for planning and control of an ego vehicle fora camera agnostic depth network, according to aspects of the presentdisclosure. Using the architecture, a controller application 202 may bedesigned such that it may cause various processing blocks of an SOC 220(for example a CPU 222, a DSP 224, a GPU 226, and/or an NPU 228) toperform supporting computations during run-time operation of thecontroller application 202.

The controller application 202 may be configured to call functionsdefined in a user space 204 that may, for example, provide for depth andpose estimation from a camera agnostic network of an ego vehicle. Thecontroller application 202 may make a request to compile program codeassociated with a library defined in a monocular depth networkapplication programming interface (API) 206 for monocular depth-awarelearning from a camera agnostic depth network for an autonomous agent.

A run-time engine 208, which may be compiled code of a runtimeframework, may be further accessible to the controller application 202.The controller application 202 may cause the run-time engine 208, forexample, to perform monocular (single-camera) 3D detection. When anobject is detected within a predetermined distance of the ego vehicle,the run-time engine 208 may in turn send a signal to an operating system210, such as a Linux Kernel 212, running on the SOC 220. The operatingsystem 210, in turn, may cause a computation to be performed on the CPU222, the DSP 224, the GPU 226, the NPU 228, or some combination thereof.The CPU 222 may be accessed directly by the operating system 210, andother processing blocks may be accessed through a driver, such asdrivers 214-218 for the DSP 224, for the GPU 226, or for the NPU 228. Inthe illustrated example, the deep neural network may be configured torun on a combination of processing blocks, such as the CPU 222 and theGPU 226, or may be run on the NPU 228, if present.

FIG. 3 is a diagram illustrating an example of a hardware implementationfor a camera agnostic monocular depth system 300, according to aspectsof the present disclosure. The camera agnostic monocular depth system300 may be configured for planning and control of an ego vehicle inresponse to monocular (single-camera) depth and pose estimation duringtraining of a car 350. The camera agnostic monocular depth system 300may be a component of a vehicle, a robotic device, or other device. Forexample, as shown in FIG. 3 , the camera agnostic monocular depth system300 is a component of the car 350. Aspects of the present disclosure arenot limited to the camera agnostic monocular depth system 300 being acomponent of the car 350, as other devices, such as a bus, motorcycle,or other like vehicle, are also contemplated for using the cameraagnostic monocular depth system 300. The car 350 may be autonomous orsemi-autonomous.

The camera agnostic monocular depth system 300 may be implemented withan interconnected architecture, represented generally by an interconnect308. The interconnect 308 may include any number of point-to-pointinterconnects, buses, and/or bridges depending on the specificapplication of the camera agnostic monocular depth system 300 and theoverall design constraints of the car 350. The interconnect 308 linkstogether various circuits including one or more processors and/orhardware modules, represented by a sensor module 302, an ego perceptionmodule 310, a processor 320, a computer-readable medium 322,communication module 324, a locomotion module 326, a location module328, a planner module 330, and a controller module 340. The interconnect308 may also link various other circuits, such as timing sources,peripherals, voltage regulators, and power management circuits, whichare well known in the art and, therefore, will not be described anyfurther.

The camera agnostic monocular depth system 300 includes a transceiver332 coupled to the sensor module 302, the ego perception module 310, theprocessor 320, the computer-readable medium 322, the communicationmodule 324, the locomotion module 326, the location module 328, aplanner module 330, and the controller module 340. The transceiver 332is coupled to an antenna 334. The transceiver 332 communicates withvarious other devices over a transmission medium. For example, thetransceiver 332 may receive commands via transmissions from a user or aremote device. As discussed herein, the user may be in a location thatis remote from the location of the car 350. As another example, thetransceiver 332 may transmit detected 3D objects and/or planned actionsfrom the ego perception module 310 to a server (not shown).

The camera agnostic monocular depth system 300 includes the processor320 coupled to the computer-readable medium 322. The processor 320performs processing, including the execution of software stored on thecomputer-readable medium 322 to provide functionality, according to thepresent disclosure. The software, when executed by the processor 320,causes the camera agnostic monocular depth system 300 to perform thevarious functions described for ego vehicle perception based onmonocular depth and pose estimation from video captured by a cameraagnostic depth network of an ego vehicle, such as the car 350, or any ofthe modules (e.g., 302, 310, 324, 326, 328, 330, and/or 340). Thecomputer-readable medium 322 may also be used for storing data that ismanipulated by the processor 320 when executing the software.

The sensor module 302 may obtain images via different sensors, such as afirst sensor 304 and a second sensor 306. The first sensor 304 may be avision sensor (e.g., a stereoscopic camera or a red-green-blue (RGB)camera) for capturing 2D RGB images. The second sensor 306 may be aranging sensor, such as a light detection and ranging (LIDAR) sensor ora radio detection and ranging (RADAR) sensor. Of course, aspects of thepresent disclosure are not limited to the aforementioned sensors, asother types of sensors (e.g., thermal, sonar, and/or lasers) are alsocontemplated for either of the first sensor 304 or the second sensor306.

The images of the first sensor 304 and/or the second sensor 306 may beprocessed by the processor 320, the sensor module 302, the egoperception module 310, the communication module 324, the locomotionmodule 326, the location module 328, and the controller module 340. Inconjunction with the computer-readable medium 322, the images from thefirst sensor 304 and/or the second sensor 306 are processed to implementthe functionality described herein. In one configuration, detected 3Dobject information captured by the first sensor 304 and/or the secondsensor 306 may be transmitted via the transceiver 332. The first sensor304 and the second sensor 306 may be coupled to the car 350 or may be incommunication with the car 350.

The location module 328 may determine a location of the car 350. Forexample, the location module 328 may use a global positioning system(GPS) to determine the location of the car 350. The location module 328may implement a dedicated short-range communication (DSRC)-compliant GPSunit. A DSRC-compliant GPS unit includes hardware and software to makethe car 350 and/or the location module 328 compliant with one or more ofthe following DSRC standards, including any derivative or fork thereof:EN 12253:2004 Dedicated Short-Range Communication—Physical layer usingmicrowave at 5.9 GHz (review); EN 12795:2002 Dedicated Short-RangeCommunication (DSRC)—DSRC Data link layer: Medium Access and LogicalLink Control (review); EN 12834:2002 Dedicated Short-RangeCommunication—Application layer (review); EN 13372:2004 DedicatedShort-Range Communication (DSRC)—DSRC profiles for RTTT applications(review); and EN ISO 14906:2004 Electronic Fee Collection—Applicationinterface.

A DSRC-compliant GPS unit within the location module 328 is operable toprovide GPS data describing the location of the car 350 with space-levelaccuracy for accurately directing the car 350 to a desired location. Forexample, the car 350 is driving to a predetermined location and desirespartial sensor data. Space-level accuracy means the location of the car350 is described by the GPS data sufficient to confirm a location of thecar 350 parking space. That is, the location of the car 350 isaccurately determined with space-level accuracy based on the GPS datafrom the car 350.

The communication module 324 may facilitate communications via thetransceiver 332. For example, the communication module 324 may beconfigured to provide communication capabilities via different wirelessprotocols, such as Wi-Fi, long term evolution (LTE), 3G, etc. Thecommunication module 324 may also communicate with other components ofthe car 350 that are not modules of the camera agnostic monocular depthsystem 300. The transceiver 332 may be a communications channel througha network access point 360. The communications channel may include DSRC,LTE, LTE-D2D, mmWave, Wi-Fi (infrastructure mode), Wi-Fi (ad-hoc mode),visible light communication, TV white space communication, satellitecommunication, full-duplex wireless communications, or any otherwireless communications protocol such as those mentioned herein.

In some configurations, the network access point 360 includes Bluetooth®communication networks or a cellular communications network for sendingand receiving data, including via short messaging service (SMS),multimedia messaging service (MMS), hypertext transfer protocol (HTTP),direct data connection, wireless application protocol (WAP), e-mail,DSRC, full-duplex wireless communications, mmWave, Wi-Fi (infrastructuremode), Wi-Fi (ad-hoc mode), visible light communication, TV white spacecommunication, and satellite communication. The network access point 360may also include a mobile data network that may include 3G, 4G, 5G, LTE,LTE-V2X, LTE-D2D, VoLTE, or any other mobile data network or combinationof mobile data networks. Further, the network access point 360 mayinclude one or more IEEE 802.11 wireless networks.

The camera agnostic monocular depth system 300 also includes the plannermodule 330 for planning a selected route/action (e.g., collisionavoidance) of the car 350 and the controller module 340 to control thelocomotion of the car 350. The controller module 340 may perform theselected action via the locomotion module 326 for autonomous operationof the car 350 along, for example, a selected route. In oneconfiguration, the planner module 330 and the controller module 340 maycollectively override a user input when the user input is expected(e.g., predicted) to cause a collision according to an autonomous levelof the car 350. The modules may be software modules running in theprocessor 320, resident/stored in the computer-readable medium 322,and/or hardware modules coupled to the processor 320, or somecombination thereof.

The National Highway Traffic Safety Administration (NHTSA) has defineddifferent “levels” of autonomous vehicles (e.g., Level 0, Level 1, Level2, Level 3, Level 4, and Level 5). For example, if an autonomous vehiclehas a higher level number than another autonomous vehicle (e.g., Level 3is a higher level number than Levels 2 or 1), then the autonomousvehicle with a higher level number offers a greater combination andquantity of autonomous features relative to the vehicle with the lowerlevel number. These different levels of autonomous vehicles aredescribed briefly below.

Level 0: In a Level 0 vehicle, the set of advanced driver assistancesystem (ADAS) features installed in a vehicle provide no vehiclecontrol, but may issue warnings to the driver of the vehicle. A vehiclewhich is Level 0 is not an autonomous or semi-autonomous vehicle.

Level 1: In a Level 1 vehicle, the driver is ready to take drivingcontrol of the autonomous vehicle at any time. The set of ADAS featuresinstalled in the autonomous vehicle may provide autonomous features suchas: adaptive cruise control (ACC); parking assistance with automatedsteering; and lane keeping assistance (LKA) type II, in any combination.

Level 2: In a Level 2 vehicle, the driver is obliged to detect objectsand events in the roadway environment and respond if the set of ADASfeatures installed in the autonomous vehicle fail to respond properly(based on the driver's subjective judgement). The set of ADAS featuresinstalled in the autonomous vehicle may include accelerating, braking,and steering. In a Level 2 vehicle, the set of ADAS features installedin the autonomous vehicle can deactivate immediately upon takeover bythe driver.

Level 3: In a Level 3 ADAS vehicle, within known, limited environments(such as freeways), the driver can safely turn their attention away fromdriving tasks, but must still be prepared to take control of theautonomous vehicle when needed.

Level 4: In a Level 4 vehicle, the set of ADAS features installed in theautonomous vehicle can control the autonomous vehicle in all but a fewenvironments, such as severe weather. The driver of the Level 4 vehicleenables the automated system (which is comprised of the set of ADASfeatures installed in the vehicle) only when it is safe to do so. Whenthe automated Level 4 vehicle is enabled, driver attention is notrequired for the autonomous vehicle to operate safely and consistentwithin accepted norms.

Level 5: In a Level 5 vehicle, other than setting the destination andstarting the system, no human intervention is involved. The automatedsystem can drive to any location where it is legal to drive and make itsown decision (which may vary based on the jurisdiction where the vehicleis located).

A highly autonomous vehicle (HAV) is an autonomous vehicle that is Level3 or higher. Accordingly, in some configurations the car 350 is one ofthe following: a Level 0 non-autonomous vehicle; a Level 1 autonomousvehicle; a Level 2 autonomous vehicle; a Level 3 autonomous vehicle; aLevel 4 autonomous vehicle; a Level 5 autonomous vehicle; and an HAV.

The ego perception module 310 may be in communication with the sensormodule 302, the processor 320, the computer-readable medium 322, thecommunication module 324, the locomotion module 326, the location module328, the planner module 330, the transceiver 332, and the controllermodule 340. In one configuration, the ego perception module 310 receivessensor data from the sensor module 302. The sensor module 302 mayreceive the sensor data from the first sensor 304 and the second sensor306. According to aspects of the present disclosure, the ego perceptionmodule 310 may receive sensor data directly from the first sensor 304 orthe second sensor 306 to perform monocular depth and pose estimationfrom images captured by the first sensor 304 or the second sensor 306 ofthe car 350, based on training using a camera agnostic network.

In robotics and 3D computer vision, a camera model that relates imagepixels and 3D world points is a prerequisite for many tasks, includingvisual odometry, depth estimation, and 3D object detection. Aperspective pinhole camera model is ubiquitous due to its simplicitybecause this camera model has few parameters and is easy to calibrate.Deep neural architectures that rely on the pinhole camera model have ledto major advances in tasks such as monocular 3D detection and depthestimation. These deep neural networks are generally trained on curatedand rectified image datasets in which a use of a pinhole camera isassumed. There are a variety of settings, however, where this assumptiondoes not hold. For example, physical arrangements of fisheye andcatadioptric lenses break the pinhole assumption (e.g., a dashboardcamera behind a windshield, or a system of multiple cameras).

The pinhole model allows for closed form lifting and projectionoperations, and thus can be easily used as a module in deeparchitectures (e.g., either fixed and precomputed or learned).Parametric distortion models, as well as models for more complex lensdesigns (e.g., fisheye cameras), are generally significantly morecomplex and harder to calibrate relative to pinhole cameras. Adaptingthese models to learn depth and ego-motion involves two majordisadvantages: (1) distortion models are generally a simplification ofcomplex lens distortion, leading to approximate correctness; and (2) newdifferentiable projection models are individually created for each typeof camera. The resulting architecture is then specialized to a singlecamera model, and is significantly modified before being used to trainon a new dataset from a novel camera.

Instead of adapting individual camera models, aspects of the presentdisclosure are directed to an end-to-end self-supervised learning of aneural camera model from raw, non-calibrated videos in addition to depthand ego-motion. The generic camera model directly relates pixels toviewing rays, allowing for a per-pixel calibration that can model anydistortion or lens system. A camera agnostic model can be trainedwithout modification on datasets captured with radically differentcameras. For example, the camera agnostic model supports learning ofdepth and ego-motion on pinhole, fisheye, and catadioptric datasets. Thecamera agnostic model can learn accurate depth maps and odometry wherethe standard perspective-based architecture (which is an incorrect modelfor non-pinhole lenses) diverges. The camera agnostic model is alearning-based self-supervised monocular technique.

As shown in FIG. 3 , the ego perception module 310 includesintrinsic/extrinsic camera information 312, a camera agnostic networkmodule 314, a monocular pose estimation module 316, and a monoculardepth estimation module 318. The camera agnostic network module 314, thecamera agnostic network module 314, the monocular pose estimation module316, and the monocular depth estimation module 318 may be components ofa same or different artificial neural network, such as a deepconvolutional neural network (CNN). The ego perception module 310 is notlimited to a deep CNN backbone.

The ego perception module 310 receives a data stream from the firstsensor 304 and/or the second sensor 306. The data stream may include a2D RGB image from the first sensor 304 and LIDAR data points from thesecond sensor 306. The data stream may include multiple frames, such asimage frames. In this configuration, the first sensor 304 capturesmonocular (single camera) 2D RGB images. The camera information from thefirst sensor 304 may be projected into a camera agnostic configurationaccording to the camera agnostic network module 314. Accordingly, thecamera agnostic network module 314 enables training of the monocularpose estimation module 316 and the monocular depth estimation module 318with a much larger dataset based on different cameras, both at trainingtime and testing time.

The ego perception module 310 is configured to perform monocular depthand pose estimation for autonomous operation of the car 350. Accordingto aspects of the present disclosure, the camera agnostic network module314 enables depth and pose estimations that can be utilized across avast array of different cameras, which may be referred to as cameraagnostic depth networks. The camera agnostic network module 314 trainsboth depth and pose estimations using a network of different cameras,both at training and testing time. Camera information used by the cameraagnostic network includes intrinsic and extrinsic camera information.For example, extrinsic parameters may define the location andorientation of the camera with respect to a world frame. Intrinsicparameters may allow a mapping between camera coordinates and pixelcoordinates in an image frame, as shown in FIGS. 4A-4C.

FIGS. 4A-4C show drawings illustrating self-supervised monocular depthand pose estimation for an array of cameras, according to aspects of thepresent disclosure. FIG. 4A illustrates an input of a monocular videoscene 400 captured with a pinhole camera (first row), a fisheye camera(second row), and a catadioptric camera (third row).

FIG. 4B illustrates a depth map of the monocular video scene 400 of FIG.4A captured with the pinhole camera (first row), the fisheye camera(second row), and the catadioptric camera (third row).

FIG. 4C illustrates a point cloud of the monocular video scene 400 ofFIG. 4A and the depth map of FIG. 4B, captured with the pinhole camera(first row), the fisheye camera (second row), and the catadioptriccamera (third row). Aspects of the present disclosure replace thestandard pinhole model generally used in self-supervised, monocularsettings with a camera agnostic network model, enabling the learning ofdepth and pose estimates for any kind of camera, as illustrated in FIGS.4A-4C.

FIG. 5 is a block diagram illustrating a self-supervised monoculardepth/pose estimation framework based on an agnostic camera network,according to aspects of the present disclosure. In one aspect of thepresent disclosure, a monocular depth/pose estimation framework 500 isused to implement the ego perception module 310 shown in FIG. 3 using,for example, a camera agnostic network. In further aspects of thepresent disclosure, the monocular depth/pose estimation framework 500enables learning of depth and pose estimates for any kind of camera, asillustrated in FIGS. 4A-4C.

In one configuration, the monocular depth/pose estimation framework 500receives two consecutive images, a target image (I_(t)) 502 and acontext image (I_(c)) 504 of a monocular video. In this configuration,the target image I_(t) 502 is provided as input to a shared encoder 510and a pose network 520. The context image 504 is also provided to thepose network 520. An output of the pose network 520 feeds an imageprojection 522 into a camera agnostic configuration. The imageprojection 522 is fed as an input to a view synthesis block 550. Thisimage projection 522 enables training of monocular depth and pose modelsusing much larger datasets by leveraging information from any camera.The context image 504 is also fed as an input to the view synthesisblock 550.

In one configuration of the monocular depth/pose estimation framework500, an output of the shared encoder 510 feeds a depth decoder 530 and aray surface decoder 540. The depth decoder 530 outputs a predicted depthmap 532 ({circumflex over (D)}_(t)), and the ray surface decoder 540outputs a predicted ray surface 542 ({circumflex over (Q)}_(t)). In thisconfiguration, the predicted depth map 532 and the predicted ray surface542 are combined, and the combination is fed as an input to the viewsynthesis block 550. The view synthesis block 550 is configured topredict a warped target image 552 (Î_(t)). In addition, the combinationof the predicted depth map 532 and the predicted ray surface 542 isprovided as feedback to a depth smoothness loss block 506, which alsoreceives the target image (I_(t)) 502 as input.

In this configuration, both the depth decoder 530 and the ray surfacedecoder 540 share the same encoder backbone (e.g., the shared encoder510). According to this aspect of the present disclosure, combining thepredicted depth map 532 ({circumflex over (D)}_(t)) with the predictedray surface 542 ({circumflex over (Q)}_(t)) enables performance of viewsynthesis by the view synthesis block 550. In this configuration, themonocular depth/pose estimation framework 500 performs self-supervisedlearning to predict the warped target image 552 (Î_(t)) by the viewsynthesis block 550. The warped target image 552 (Î_(t)) is provided asfeedback to a photometric loss block 508, which also receives the targetimage (I_(t)) 502 as input. The monocular depth/pose estimationframework 500 is described in further detail below.

1. Self-Supervised Depth and Pose Estimation

In a self-supervised monocular structure-from-motion setting, anobjective is to learn: (a) a depth model fd: I→D, that predicts a depthvalue {circumflex over (d)}=f_(d)(I(p)) for every pixel p=[u, v]^(T) inthe target image I_(t) (up to a scale factor); and (b) an ego-motionmodel f_(x): (I_(t), I_(C))→X_(t→C), that predicts the rigidtransformations for all c∈C given by

$\begin{matrix}{{X_{t\rightarrow C} = {\begin{pmatrix}R & t \\0 & 1\end{pmatrix} \in {S{E(3)}}}},} & \;\end{matrix}$between the target image It and a set of context images I_(c)∈Ic, takenas adjacent frames in a video sequence.1.1 Objective Loss

As shown in FIG. 5 , the monocular depth/pose estimation framework 500relies on a depth network (e.g., 510, 530, and 540) and a pose network520 that are simultaneously trained in a self-supervised manner. Thissimultaneous training may be performed by projecting pixels in the imageprojection 522 from the context image I_(c) 504 onto the target imageI_(t), 502. The simultaneous training includes minimizing thephotometric re-projection error between the original target image I_(t)502 and the warped target image 552 Î_(t) (e.g., synthesized images). Animage synthesis operation of view synthesis block 550 is performedusing, for example, spatial transformer networks (STNs), via gridsampling with bilinear interpolation. As a result, the image synthesisoperation is fully differentiable. This pixel-wise warping to form thewarped target image 552 Î_(t) is depicted in FIG. 6A (described below)and takes the form of:{circumflex over (p)} _(t)=π_(c)(R _(t→c)ϕ_(t)(p _(t) ,d _(t))+t_(t→c))  (1)where ϕ(p, d)=P is responsible for the lifting of an image pixel inhomogeneous coordinates p=[u, v, 1]^(T) to a 3D point P=[x, y, z]^(T)given its depth value d. Conversely, π(P)=p projects a 3D point backonto the image plane as a pixel. For the standard pinhole camera model,used in most of the current learning-based monocular depth estimationalgorithms, these functions have a closed-form solution and can becalculated as:

$\begin{matrix}{{{\varnothing\left( {p,d} \right)} = {{dK^{- 1}p} = {{d\begin{bmatrix}f_{x} & 0 & c_{x} \\0 & f_{y} & c_{y} \\0 & 0 & 1\end{bmatrix}}^{- 1}\left\lbrack {u\mspace{14mu} v\mspace{14mu} 1} \right\rbrack}^{T}}},} & (2) \\{{{\pi(P)} = {{\frac{1}{P_{z}}KP} = {{\frac{1}{z}\begin{bmatrix}f_{x} & 0 & c_{x} \\0 & f_{y} & c_{y} \\0 & 0 & 1\end{bmatrix}}\left\lbrack {x\mspace{14mu} y\mspace{14mu} z} \right\rbrack}^{T}}},} & (3)\end{matrix}$with intrinsic matrix K, focal length (f_(x), f_(y)) and principal point(c_(x), c_(y)). These parameters are usually assumed to be known,obtained using prior independent calibration techniques, or are learnedas additional variables during the training stage. The self-supervisedobjective loss to be minimized is of the form:

=(I _(t) ,Î _(t))=

_(p)(I _(t) ,I _(C))+λ_(d)

_(d)({circumflex over (D)} _(t)),  (4)which is the combination of an appearance-based loss

_(p) and a weighted depth smoothness loss

_(d), described below in more detail. This loss is then averaged perpixel and batch during training to produce the final value to minimize.For simplicity, dynamic objects (which break the static sceneassumption) are not explicitly modeled, although these could be easilyincorporated into our framework to further improve experimental results.

Appearance-Based Loss. The similarity between target I_(t) and warpedÎ_(t) images is estimated at the pixel level using structural similarity(SSIM) combined with an L1 loss term:

$\begin{matrix}{{\mathcal{L}_{p}\left( {I_{t},{\overset{\hat{}}{I}}_{t}} \right)} = {{\alpha\frac{1 - {S\; S\; I\;{M\left( {I_{t},{\overset{\hat{}}{I}}_{t}} \right)}}}{2}} + {\left( {1 - \alpha} \right){{I_{t},{\overset{\hat{}}{I}}_{t}}}}}} & (5)\end{matrix}$In order to increase robustness against parallax or the presence ofdynamic objects, a minimum pixel-wise photometric loss value isconsidered for each context image in I_(C). The intuition is that thesame pixel will not be occluded or out-of-bounds in all context images,and its association with minimal photometric loss should be correct.Similarly, we mask out static pixels by removing those with a warpedphotometric loss

_(p)(I_(t), Î_(t)) higher than their original photometric loss

_(p)(I_(t), I_(c)).Depth Smoothness Loss. To regularize the depth in image regions withouttexture, aspects of the present disclosure incorporate an edge-awareterm that penalizes high depth gradients in areas with low colorgradients:

_(s)({circumflex over (D)} _(t))=|δ_(x) {circumflex over (D)} _(t) |e^(−|δ) ^(x) ^(I) ^(t) ^(|)+|δ_(y) {circumflex over (D)} _(t) |e ^(−|δ)^(y) ^(I) ^(t) ^(|)  (6)2. Camera Agnostic Model

The monocular depth/pose estimation framework 500 is configuredaccording to a camera agnostic model for a self-supervised learningframework capable of jointly estimating depth and pose in an end-to-enddifferentiable manner. Our method can be trained on raw unlabeled videoscaptured from a wide variety of camera geometries without anycalibration or architectural modification, thus broadening the use ofself-supervised learning in the wild. The monocular depth/poseestimation framework 500 enables visual odometry and depth estimation onpinhole, fisheye, and catadioptric cameras, although other types ofcameras are contemplated.

FIGS. 6A and 6B illustrate lifting (ϕ) and projection (π) operations fora standard pinhole model 600 in FIG. 6A and a camera agnostic model 650in FIG. 6B, according to aspects of the present disclosure. The lifting(ϕ) and projection (π) operations according to Equation (1) are shownfor a single pixel p_(j) 652, considering a target image I_(t) 502 andcontext image I_(c) 504. As shown in FIG. 6B, straight arrows representunitary ray surface vectors Q(p), drawn out of scale to facilitatevisualization. In this example, p₁ 654 is associated with the singlepixel p_(j) 652 because p₁ 654 satisfies Equation (9), as described infurther detail below.

Generally, a camera model may be defined by two operations: the liftingof 3D points from image pixels (e.g., ϕ(p_(, d))=P); and the projectionof 3D points onto the image plane (e.g., π(P)=p). A standard pinholeperspective model may provide simple closed-form solutions to these twooperations, as matrix-vector products (see Equations (2)-(3) and FIG.6A). In a generic camera model, the camera model is composed of a raysurface that associates each pixel with a corresponding direction,offering a completely generic association between 3D points and imagepixels. In this model, although lifting is simple and can be computed inclosed form, the projection operation has no closed-form solution and isnon-differentiable, which makes it unsuitable for learning-basedapplications, as shown in FIG. 6B. Below, a variant of the genericcamera model is described that is differentiable and, thus, amenable toend-to-end learning in a self-supervised monocular setting.

2.1 Notation

According to aspects of the present disclosure, monocular depth/poseestimation may be formulated as follows. The following notation is used:for each pixel p=[u, v]^(T), a corresponding camera center S(u, v) isintroduced as a 3D point and a unitary ray surface vector Q(u, v)∈

³, with D(u, v) representing the scene depth along the ray. Note that,for central cameras, the camera center is the same for all points, sothat S(u, v)=S, ∀(u, v). The monocular depth/pose estimation framework500, shown in FIG. 5 represents the full training architecture accordingto aspects of the present disclosure. The monocular depth/poseestimation framework 500 is configured to produce a ray surfaceestimate, f_(r): I→Q, by adding the ray surface decoder 540 to the depthdecoder 530, in which the ray surface decoder 540 is configured togenerate the predicted ray surface 542 ({circumflex over (Q)}=f_(r)(I)).

2.2 Lifting

In aspects of the present disclosure, given the above definitions, forany pixel p, a corresponding 3D point P as follows:P(u,v)=S(u,v)+{circumflex over (D)}(u,v){circumflex over (Q)}(u,v)  (7)In other words, the predicted ray vector {circumflex over (Q)}(u, v) isscaled and offset by the camera center S(u, v), which is the same forall pixels in a central camera. Nevertheless, because the monoculardepth/pose estimation framework 500 operates in a self-supervisedmonocular learning-based setting, the resulting depth and pose estimatesare generated up to a scale factor. In other words, for simplicity andwithout loss of generality, the camera center is assumed to coincidewith the origin of the reference coordinate system and set S(u, v)=[0,0, 0]^(T) ∀ u, v∈I.2.3 Projection

Consider

𝒫_(t) = {P_(j)}_(j = 1)^(HW)602,produced by lifting pixels from the target image I_(t) 502 as 3D points.In the standard pinhole camera model of FIG. 6A, projection is a simplematrix-vector product (Equation 3). For the proposed neural cameramodel, however, for each 3D point (e.g., single pixel P_(j) 652), thecorresponding pixel p_(i)∈I_(c) 656 with a ray surface vector{circumflex over (Q)}_(i)={circumflex over (Q)}_(c)(p_(i)) 670 that mostclosely matches the direction of the single pixel P_(j) 652 to a cameracenter S_(c) 660 is computed, as shown in FIG. 6B. This direction may bereferred to r_(c→j)=P_(j)−S_(c) 680. Thus, p*_(i) is computed such that:

$\begin{matrix}{p_{i}^{*} = {\underset{{pi}\mspace{14mu} \in \mspace{14mu}{Ic}}{\arg\mspace{14mu}\max}\left\langle {{{\hat{Q}}_{c}\left( P_{i} \right)},r_{c\rightarrow j}} \right\rangle}} & (8)\end{matrix}$Solving this problem involves searching over the entire ray surface{circumflex over (Q)}_(c) and can be computationally expensive: a cameraproducing images of resolution H×W would involve a total of (HW)²evaluations, as each 3D point from

_(t) can be associated with any pixel from I_(c). Additionally, theargmax operation is non-differentiable, which precludes its use in anend-to-end learning-based setting. Solutions are described to each ofthese issues below, that in conjunction enable the simultaneous learningof depth, pose, and the proposed neural camera model in a fullyself-supervised monocular setting.Softmax Approximation. To project the 3D points

_(t) 602 onto the context image I_(c) 504, for each p_(j)∈

_(t), the corresponding pixel p_(i)∈I_(c) 656 is determined with asurface ray {circumflex over (Q)}_(i) 670 closest to the directionr_(c→j)=P_(j)−S_(c) 680. Taking the dot product of each directionr_(c→j) 680 with each ray vector {circumflex over (Q)}_(i) 670, an(H×W)² tensor M is obtained, in which each coefficient M_(ij)=

{circumflex over (Q)}_(i), r_(c→j)

=M(p_(i)P_(j)) represents the similarity between {circumflex over(Q)}_(i) 670 and r_(c→j) 680. Using this notation, projection for theproposed neural camera model is given by selecting the i* index for eachsingle pixel P_(j) 652 with:

$\begin{matrix}{i*={\arg\mspace{14mu}{\max\limits_{i}\mspace{14mu}{M\left( {p_{i},P_{j}} \right)}}}} & (9)\end{matrix}$To make this projection operation differentiable, argmax with a softmaxare substituted with a temperature τ, thus obtaining a new tensor {tildeover (M)} defined as:

$\begin{matrix}{{\overset{\sim}{M}\left( {p_{i},P_{j}} \right)} = \frac{\exp\left( {{M\left( {p_{i},P_{j}} \right)}/\tau} \right)}{\left( {\Sigma_{i}{\exp\left( {{M\left( {p_{i},P_{j}} \right)}/\tau} \right)}} \right)}} & (10)\end{matrix}$The temperature is annealed over time so that the tensor becomesapproximately one-hot for each pixel. A 2D-3D association is obtainedand used for projection by multiplying with a vector of pixel indices.Thus, projection can now be implemented in a fully differentiable mannerusing, for example, spatial transformer networks (STNs).Residual Ray Surface Template. In the structure-from-motion setting,learning a randomly initialized ray surface is similar to learning 3Dscene flow, which is a challenging problem when no calibration isavailable, particularly when considering self-supervision. To avoid thisrandom initialization, instead a residual ray surface {circumflex over(Q)}_(r) is learned and added to a fixed ray surface template Q₀ toproduce {circumflex over (Q)}=Q₀+λ_(r) {circumflex over (Q)}_(r). Theintroduction of such a template allows the injection of geometric priorsinto the learning framework, since if some form of camera calibration isknown—even if only an approximation—the corresponding ray surface isgenerated and used as a starting point for further refinement using thelearned ray surface residual. If no such information is available, a“dummy” template is initialized based on a pinhole camera model,obtained by lifting a plane at a fixed distance (e.g., Equation (2)) andthe surface is normalized. For stability, training is initiated withsolely the template Q₀, with the gradual introduction of the residual{circumflex over (Q)}_(r), by increasing the value of λ_(r).Interestingly, this pinhole prior significantly improves trainingstability and convergence speed even in a decidedly non-pinhole setting(e.g., catadioptric cameras).Patch-Based Data Association. In the most general version of theproposed neural camera model, rays at each pixel are independent and canpoint in completely different directions. Consequently, Equation (9)specifies searching over the entire image, which quickly becomescomputationally infeasible at training time even for lower resolutionimages, both in terms of speed and memory footprint. To alleviate suchheavy burden, the optimal projection search (e.g., Equation (10)) isrestricted to a small h×w grid in the context image I_(c) 504surrounding the (u, v) coordinates of the target pixel p_(t) 602. Themotivation, in most cases, is based on the assumption that camera motionis small enough to produce correct associations within thisneighborhood, especially when using the residual ray surface templatedescribed above. To further reduce memory burdens, the search isperformed on the predicted ray surface at half-resolution, which is thenupsampled using bilinear interpolation to produce pixel-wise estimates.At test-time none of these approximations are necessary, and afull-resolution ray surface is predicted directly from the input image.

FIG. 7 is a flowchart illustrating a method for monocular depth/poseestimation in a camera agnostic network, according to aspects of thepresent disclosure. The method 700 begins at block 702, in which amonocular depth model and a monocular pose network are trained to learnmonocular depth estimation and monocular pose estimation based on atarget image and context image from monocular video captured bydifferent cameras. For example, as shown in FIG. 5 , the monoculardepth/pose estimation framework 500 relies on a depth network (e.g.,510, 530 and 540) and a pose network 520 that are simultaneously trainedin a self-supervised manner. This simultaneous training may be performedby projecting pixels in the image projection 522 from the context imageI_(c) 504 onto the target image I_(t), 502. The simultaneous trainingincludes minimizing the photometric re-projection error between theoriginal target image I_(t) 502 and the warped target image 552 Î_(t)(e.g., synthesized).

At block 704, 3D points are lifted from image pixels of the target imageaccording to the one or more context images. At block 706, the lifted 3Dpoints are projected onto an image plane according to a predicted rayvector based on the monocular depth model, the monocular pose model, anda camera center according to the camera agnostic network. For example,as shown in FIG. 6B, for each 3D point (e.g., single pixel P_(j) 652),the corresponding pixel p_(i)∈I_(c) 656 with a ray surface vector{circumflex over (Q)}_(i)={circumflex over (Q)}_(c)(p_(i)) 670 that mostclosely matches the direction of the single pixel P_(j) 652 to a cameracenter S_(c) 660 is computed. This direction may be referred to asr_(c→j)=P_(j)−S_(c) 680.

At block 708, a warped target image is predicted from a predicted depthmap of the monocular depth module, a ray surface of the predicted rayvector, and a projection of the lifted 3D points according to the cameraagnostic configuration. For example, as shown in FIG. 5 , the monoculardepth/pose estimation framework 500 relies on a depth network (e.g.,510, 530 and 540) and a pose network 520 that are simultaneously trainedin a self-supervised manner. This simultaneous training may be performedby projecting pixels in the image projection 522 from the context imageI_(c) 504 onto the target image I_(t), 502. The simultaneous trainingincludes minimizing the photometric re-projection error between theoriginal target image I_(t) 502 and warped target image 552 Î_(t). Animage synthesis operation of view synthesis block 550 is performedusing, for example, spatial transformer networks (STNs), via gridsampling with bilinear interpolation.

The method 700 may also include estimating a trajectory of an egovehicle based on the warped target image. For example, as shown in FIG.5 , in the monocular depth/pose estimation framework 500 the warpedtarget image 552 predicts rigid transformations between the target imageand the one or more context images of adjacent frames of monocular videoto estimate ego motion of an ego vehicle. The method 700 also includesplanning a trajectory of an ego vehicle according to the estimatedtrajectory of the ego vehicle, for example, as performed by the plannermodule 330 and/or the controller module 340 shown in FIG. 3 . Egovehicle perception using monocular depth/pose estimation framework 500for monocular ego-motion estimation from a single camera of the car 350is beneficially improved according to aspects of the present disclosure.

Aspects of the present disclosure provide a modification to standarddepth and pose estimations by incorporating camera information.Incorporating camera information into standard depth and poseestimations leads to estimates that are much more robust. The modifieddepth and pose estimations use a network of different cameras, both attraining and testing time. This camera network enables training ofmonocular depth and pose models using much larger datasets. According toaspects of the present disclosure, the larger datasets leverageinformation from any camera in a similar way by projecting thisinformation into a camera agnostic configuration. The camera informationincludes intrinsic and extrinsic camera information. For example,extrinsic parameters may define the location and orientation of thecamera with respect to a world frame. Intrinsic parameters may allow amapping between camera coordinates and pixel coordinates in an imageframe.

In some aspects of the present disclosure, the method 700 may beperformed by the SOC 100 (FIG. 1 ) or the software architecture 200(FIG. 2 ) of the ego vehicle 150 (FIG. 1 ). That is, each of theelements of method 700 may, for example, but without limitation, beperformed by the SOC 100, the software architecture 200, or theprocessor (e.g., CPU 102), and/or other components included therein ofthe ego vehicle 150.

The various operations of methods described above may be performed byany suitable means capable of performing the corresponding functions.The means may include various hardware and/or software component(s)and/or module(s), including, but not limited to, a circuit, anapplication-specific integrated circuit (ASIC), or processor. Generally,where there are operations illustrated in the figures, those operationsmay have corresponding counterpart means-plus-function components withsimilar numbering.

As used herein, the term “determining” encompasses a wide variety ofactions. For example, “determining” may include calculating, computing,processing, deriving, investigating, looking up (e.g., looking up in atable, a database or another data structure), ascertaining, and thelike. Additionally, “determining” may include receiving (e.g., receivinginformation), accessing (e.g., accessing data in a memory), and thelike. Furthermore, “determining” may include resolving, selecting,choosing, establishing, and the like.

As used herein, a phrase referring to “at least one of” a list of itemsrefers to any combination of those items, including single members. Asan example, “at least one of: a, b, or c” is intended to cover: a, b, c,a-b, a-c, b-c, and a-b-c.

The various illustrative logical blocks, modules, and circuits describedin connection with the present disclosure may be implemented orperformed with a processor configured according to the presentdisclosure, a digital signal processor (DSP), an application-specificintegrated circuit (ASIC), a field-programmable gate array signal (FPGA)or other programmable logic device (PLD), discrete gate or transistorlogic, discrete hardware components, or any combination thereof designedto perform the functions described herein. The processor may be amicroprocessor, but in the alternative, the processor may be anycommercially available processor, controller, microcontroller, or statemachine specially configured as described herein. A processor may alsobe implemented as a combination of computing devices, e.g., acombination of a DSP and a microprocessor, a plurality ofmicroprocessors, one or more microprocessors in conjunction with a DSPcore, or any other such configuration.

The steps of a method or algorithm described in connection with thepresent disclosure may be embodied directly in hardware, in a softwaremodule executed by a processor, or in a combination of the two. Asoftware module may reside in any form of storage medium that is knownin the art. Some examples of storage media may include random accessmemory (RAM), read only memory (ROM), flash memory, erasableprogrammable read-only memory (EPROM), electrically erasableprogrammable read-only memory (EEPROM), registers, a hard disk, aremovable disk, a CD-ROM, and so forth. A software module may comprise asingle instruction, or many instructions, and may be distributed overseveral different code segments, among different programs, and acrossmultiple storage media. A storage medium may be coupled to a processorsuch that the processor can read information from, and write informationto, the storage medium. In the alternative, the storage medium may beintegral to the processor.

The methods disclosed herein comprise one or more steps or actions forachieving the described method. The method steps and/or actions may beinterchanged with one another without departing from the scope of theclaims. In other words, unless a specific order of steps or actions isspecified, the order and/or use of specific steps and/or actions may bemodified without departing from the scope of the claims.

The functions described may be implemented in hardware, software,firmware, or any combination thereof. If implemented in hardware, anexample hardware configuration may comprise a processing system in adevice. The processing system may be implemented with a busarchitecture. The bus may include any number of interconnecting busesand bridges depending on the specific application of the processingsystem and the overall design constraints. The bus may link togethervarious circuits including a processor, machine-readable media, and abus interface. The bus interface may connect a network adapter, amongother things, to the processing system via the bus. The network adaptermay implement signal processing functions. For certain aspects, a userinterface (e.g., keypad, display, mouse, joystick, etc.) may also beconnected to the bus. The bus may also link various other circuits suchas timing sources, peripherals, voltage regulators, power managementcircuits, and the like, which are well known in the art, and therefore,will not be described any further.

The processor may be responsible for managing the bus and processing,including the execution of software stored on the machine-readablemedia. Examples of processors that may be specially configured accordingto the present disclosure include microprocessors, microcontrollers, DSPprocessors, and other circuitry that can execute software. Softwareshall be construed broadly to mean instructions, data, or anycombination thereof, whether referred to as software, firmware,middleware, microcode, hardware description language, or otherwise.Machine-readable media may include, by way of example, RAM, flashmemory, ROM, programmable read-only memory (PROM), EPROM, EEPROM,registers, magnetic disks, optical disks, hard drives, or any othersuitable storage medium, or any combination thereof. Themachine-readable media may be embodied in a computer-program product.The computer-program product may comprise packaging materials.

In a hardware implementation, the machine-readable media may be part ofthe processing system separate from the processor. However, as thoseskilled in the art will readily appreciate, the machine-readable media,or any portion thereof, may be external to the processing system. By wayof example, the machine-readable media may include a transmission line,a carrier wave modulated by data, and/or a computer product separatefrom the device, all of which may be accessed by the processor throughthe bus interface. Alternatively, or in addition, the machine-readablemedia, or any portion thereof, may be integrated into the processor,such as the case may be with cache and/or specialized register files.Although the various components discussed may be described as having aspecific location, such as a local component, they may also beconfigured in various ways, such as certain components being configuredas part of a distributed computing system.

The processing system may be configured with one or more microprocessorsproviding the processor functionality and external memory providing atleast a portion of the machine-readable media, all linked together withother supporting circuitry through an external bus architecture.Alternatively, the processing system may comprise one or moreneuromorphic processors for implementing the neuron models and models ofneural systems described herein. As another alternative, the processingsystem may be implemented with an application-specific integratedcircuit (ASIC) with the processor, the bus interface, the userinterface, supporting circuitry, and at least a portion of themachine-readable media integrated into a single chip, or with one ormore field-programmable gate arrays (FPGAs), programmable logic devices(PLDs), controllers, state machines, gated logic, discrete hardwarecomponents, or any other suitable circuitry, or any combination ofcircuits that can perform the various functions described throughout thepresent disclosure. Those skilled in the art will recognize how best toimplement the described functionality for the processing systemdepending on the particular application and the overall designconstraints imposed on the overall system.

The machine-readable media may comprise a number of software modules.The software modules include instructions that, when executed by theprocessor, cause the processing system to perform various functions. Thesoftware modules may include a transmission module and a receivingmodule. Each software module may reside in a single storage device or bedistributed across multiple storage devices. By way of example, asoftware module may be loaded into RAM from a hard drive when atriggering event occurs. During execution of the software module, theprocessor may load some of the instructions into cache to increaseaccess speed. One or more cache lines may then be loaded into a specialpurpose register file for execution by the processor. When referring tothe functionality of a software module below, it will be understood thatsuch functionality is implemented by the processor when executinginstructions from that software module. Furthermore, it should beappreciated that aspects of the present disclosure result inimprovements to the functioning of the processor, computer, machine, orother system implementing such aspects.

If implemented in software, the functions may be stored or transmittedover as one or more instructions or code on a non-transitorycomputer-readable medium. Computer-readable media include both computerstorage media and communication media including any medium thatfacilitates transfer of a computer program from one place to another. Astorage medium may be any available medium that can be accessed by acomputer. By way of example, and not limitation, such computer-readablemedia can comprise RAM, ROM, EEPROM, CD-ROM or other optical diskstorage, magnetic disk storage or other magnetic storage devices, or anyother medium that can carry or store desired program code in the form ofinstructions or data structures and that can be accessed by a computer.Additionally, any connection is properly termed a computer-readablemedium. For example, if the software is transmitted from a website,server, or other remote source using a coaxial cable, fiber optic cable,twisted pair, digital subscriber line (DSL), or wireless technologiessuch as infrared (IR), radio, and microwave, then the coaxial cable,fiber optic cable, twisted pair, DSL, or wireless technologies such asinfrared, radio, and microwave are included in the definition of medium.Disk and disc, as used herein, include compact disc (CD), laser disc,optical disc, digital versatile disc (DVD), floppy disk, and Blu-ray®disc; where disks usually reproduce data magnetically, while discsreproduce data optically with lasers. Thus, in some aspectscomputer-readable media may comprise non-transitory computer-readablemedia (e.g., tangible media). In addition, for other aspects,computer-readable media may comprise transitory computer-readable media(e.g., a signal). Combinations of the above should also be includedwithin the scope of computer-readable media.

Thus, certain aspects may comprise a computer program product forperforming the operations presented herein. For example, such a computerprogram product may comprise a computer-readable medium havinginstructions stored (and/or encoded) thereon, the instructions beingexecutable by one or more processors to perform the operations describedherein. For certain aspects, the computer program product may includepackaging material.

Further, it should be appreciated that modules and/or other appropriatemeans for performing the methods and techniques described herein can bedownloaded and/or otherwise obtained by a user terminal and/or basestation as applicable. For example, such a device can be coupled to aserver to facilitate the transfer of means for performing the methodsdescribed herein. Alternatively, various methods described herein can beprovided via storage means (e.g., RAM, ROM, a physical storage mediumsuch as a CD or floppy disk, etc.), such that a user terminal and/orbase station can obtain the various methods upon coupling or providingthe storage means to the device. Moreover, any other suitable techniquefor providing the methods and techniques described herein to a devicecan be utilized.

It is to be understood that the claims are not limited to the preciseconfiguration and components illustrated above. Various modifications,changes, and variations may be made in the arrangement, operation, anddetails of the methods and apparatus described above without departingfrom the scope of the claims.

What is claimed is:
 1. A method for monocular depth/pose estimation in acamera agnostic network, the method comprising: projecting lifted 3Dpoints onto an image plane according to a predicted ray vector based ona monocular depth model, a monocular pose model, and a camera centeraccording to the camera agnostic network; and predicting a warped targetimage from a predicted depth map of the monocular depth model, a raysurface of the predicted ray vector, and a projection of the lifted 3Dpoints according to the camera agnostic network.
 2. The method of claim1, further comprising: training the monocular depth model and themonocular pose model to learn monocular depth estimation and monocularpose estimation based on a target image and one or more context imagesfrom monocular video captured by the camera agnostic network; andlifting 3D points from image pixels of the target image according to theone or more context images.
 3. The method of claim 2, in which trainingcomprises self-supervised learning of an ego-motion model to predictrigid transformations between the target image and the one or morecontext images captured by the camera agnostic network, in which the oneor more context images represent adjacent frames of monocular video. 4.The method of claim 2, in which training comprises incorporatingintrinsic parameters and extrinsic parameters regarding a plurality ofdifferent cameras into a camera agnostic configuration of the monoculardepth model and the monocular pose model.
 5. The method of claim 4, inwhich the extrinsic parameters define a location and orientation of theplurality of different cameras with respect to a world frame, and theintrinsic parameters enable a mapping between camera coordinates andpixel coordinates in an image frame.
 6. The method of claim 1, furthercomprising estimating a pose transformation from a target image to oneor more context images based on predicted rigid transformations betweenthe target image and the one or more context images captured by thecamera agnostic network, in which the one or more context imagesrepresent adjacent frames of monocular video.
 7. The method of claim 1,further comprising estimating a trajectory of an ego vehicle based onthe warped target image.
 8. The method of claim 7, further comprisingplanning a trajectory of the ego vehicle according to an estimatedtrajectory of the ego vehicle.
 9. The method of claim 1, furthercomprising: scaling the predicted ray vector according to a predicteddepth; and offsetting the predicted ray vector by the camera center. 10.A non-transitory computer-readable medium having program code recordedthereon for monocular depth/pose estimation in a camera agnosticnetwork, the program code being executed by a processor and comprising:program code to project lifted 3D points onto an image plane accordingto a predicted ray vector based on a monocular depth model, a monocularpose model, and a camera center according to the camera agnosticnetwork; and program code to predict a warped target image from apredicted depth map of the monocular depth model, a ray surface of thepredicted ray vector, and a projection of the lifted 3D points accordingto the camera agnostic network.
 11. The non-transitory computer-readablemedium of claim 10, further comprising: program code to train themonocular depth model and the monocular pose model to learn monoculardepth estimation and monocular pose estimation based on a target imageand one or more context images from monocular video captured by thecamera agnostic network; program code to lift 3D points from imagepixels of the target image according to the one or more context images.12. The non-transitory computer-readable medium of claim 11, in whichtraining comprises self-supervised learning of an ego-motion model topredict rigid transformations between the target image and the one ormore context images captured by the camera agnostic network, in whichthe one or more context images represent adjacent frames of monocularvideo.
 13. The non-transitory computer-readable medium of claim 11, inwhich the program code to train comprises program code to incorporateintrinsic parameters and extrinsic parameters regarding a plurality ofdifferent cameras into a camera agnostic configuration of the monoculardepth model and the monocular pose model, and in which the extrinsicparameters define a location and orientation of the plurality ofdifferent cameras with respect to a world frame, and the intrinsicparameters enable a mapping between camera coordinates and pixelcoordinates in an image frame.
 14. The non-transitory computer-readablemedium of claim 10, further comprising estimating a pose transformationfrom a target image to one or more context images based on predictedrigid transformations between the target image and the one or morecontext images captured by the camera agnostic network, in which the oneor more context images represent adjacent frames of monocular video. 15.The non-transitory computer-readable medium of claim 10, furthercomprising: program code to estimate a trajectory of an ego vehiclebased on the warped target image; and program code to plan a trajectoryof the ego vehicle according to an estimated trajectory of the egovehicle.
 16. The non-transitory computer-readable medium of claim 10,further comprising: program code to scale the predicted ray vectoraccording to a predicted depth; and program code to offset the predictedray vector by the camera center.
 17. A system for monocular depth/poseestimation in a camera agnostic network, the system comprising: a posenetwork to project lifted 3D points onto an image plane according to apredicted ray vector based on a monocular depth model, a monocular posemodel, and a camera center according to the camera agnostic network; anda view synthesis block to predict a warped target image from a predicteddepth map of the monocular depth model, a ray surface of the predictedray vector, and a projection of the lifted 3D points according to thecamera agnostic network.
 18. The system of claim 17, further comprisingan ego perception module to estimate a trajectory of an ego vehiclebased on the warped target image.
 19. The system of claim 18, furthercomprising a planner module to plan a trajectory of the ego vehicleaccording to an estimated trajectory of the ego vehicle.
 20. The systemof claim 17, further comprising: a depth network to train the monoculardepth model and the monocular pose model to learn monocular depthestimation and monocular pose estimation based on a target image and oneor more context images from monocular video captured by the cameraagnostic network, in which the pose network is further to lift 3D pointsfrom image pixels of the target image according to the one or morecontext images.